1. Technical Field
The present invention relates to a sequence generator, and is suitable particularly but not exclusively for generating sequences of elements such as numbers or task names.
2. Description of Related Art
Machine learning systems are, among other things, applied as a mechanism for learning information that can then be applied to efficiently automate and control various processes such as control of a plant, prediction of markets, scheduling of tasks, understanding of spoken text, or of datamining applications. Examples of applications that use machine learning in this way include load-balancing systemsi for a distributed computer system, where load-balancing parameters are learnt and applied to achieve efficient scheduling of tasks within the computer system, and systems for learning sequences to create planning solutions and schedule system or user tasks. The latter embeds Temporal Sequence Storage and Generation (TSSG), which is based on observations of sequence retention by biological systems, of learnt sequences into the system to create a schedule of tasks.
Neural networks, which are artificial systems comprising a number of nodes connected by links, are ideally suited to model TSSG, as they can be trained using inductive learning algorithms. Current neural networks that achieve TSSG include time-delay netsii, which implement Hakens embedding theorem, and various recurrent models, such as the spin-glassiii; nets with context neuronesiv; the Elman net and extensions thereofv; and the crumbling history approachvi. However, the spin-glass is limited to reproducing short sequences, the Elman net uses associative chaining to generate sequences, which is computationally expensive, and the crumbling history approach cannot plan successfully using learnt sequences. In all of these cases, the ability of the neural networks to usefully be integrated into systems that perform complex scheduling tasks such as work pattern management is limited. U.S. Pat. No. 5,434,783 discloses a control system for controlling noise and/or vibration in an automotive vehicle. The control system utilises a control neural network and an identification neural network arranged in a hierarchical relationship, where output from the control neural network is used to control a loud speaker and piezoelectric actuator. The identification neural network receives output signals from the control neural net together with signals from an in-vehicle microphone and outputs a control predicted value. This control predicted value is then compared with a control target value and the result of the comparison is used to correct the connection weights of the control neural network.